Image processing system for vehicle damage

ABSTRACT

An image processing system automatically processes a plurality of images of a damaged vehicle and determines replacements parts that are needed to repair the vehicle therefrom. The system includes a network interface via which the images are received, an image extraction component that generates a set of image attributes from the received images, a parts identifier component that generates indications of the needed replacement parts based on the image attributes and an information identification model, and an output interface via which the generated indications are provided. Additionally, the system includes a data storage entity storing data from multiple historical vehicle insurance claims, and a model generation component that generates the information identification model based on the historical claim data. The information identification model includes independent variable(s) corresponding to a set of image attributes that are more strongly correlated to replacement parts than are other attributes of the historical claim data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 14/218,165, entitled “SYSTEM AND METHOD OF PREDICTING VEHICLECLAIM INFORMATION BASED ON IMAGE ATTRIBUTES” and filed on Mar. 18, 2014,the entire disclosure of which is hereby incorporated by referenceherein.

This application is related to commonly-owned U.S. patent applicationSer. No. 14/218,148, entitled “SYSTEM AND METHOD OF PREDICTING VEHICLECLAIM INFORMATION BASED ON DEFORMATION IMAGES” and filed on Mar. 18,2014, the entire disclosure of which is hereby incorporated by referenceherein. Additionally, this application is related to commonly-owned U.S.Pat. No. 8,239,220 entitled “METHOD AND APPARATUS FOR OBTAININGPHOTOGRAMMETRIC DATA TO ESTIMATE IMPACT SEVERITY,” and to commonly-ownedU.S. Pat. No. 8,095,391 entitled “SYSTEM AND METHOD FOR PERFORMINGREINSPECTION IN INSURANCE CLAIM PROCESSING,” and to commonly-owned U.S.Pat. No. 9,218,626 entitled “AUTOMATIC PREDICTION AND RECOMMENDATION OFPARTS, MATERIALS, AND SERVICES FOR VEHICLE INSURANCE CLAIM ESTIMATES ANDSUPPLEMENTS,” the entire disclosures of which are hereby incorporated byreference herein.

Further, this application is related to commonly-owned U.S. patentapplication Ser. No. 12/792,104, entitled “SYSTEMS AND METHODS OFPREDICTING VEHICLE CLAIM COST” and filed on Jun. 2, 2010; tocommonly-owned U.S. patent application Ser. No. 14/168,345, entitled“SYSTEM AND METHOD OF PREDICTING A VEHICLE CLAIM SUPPLEMENT BETWEEN ANINSURANCE CARRIER AND A REPAIR FACILITY” and filed on Jan. 30, 2014; andto commonly-owned U.S. patent application Ser. No. 14/168,327, entitled“SYSTEMS AND METHODS OF PREDICTING VEHICLE CLAIM RE-INSPECTIONS” andfiled on Jan. 30, 2014, the entire disclosures of which are herebyincorporated by reference herein.

Still further, this application is related to commonly-owned U.S. patentapplication Ser. No. 15/016,756, entitled “IMAGING PROCESSING SYSTEM FORIDENTIFYING PARTS FOR REPAIRING A VEHICLE” and filed on Feb. 5, 2016;and to commonly-owned U.S. patent application Ser. No. 15/079,380entitled “IMAGE PROCESSING SYSTEM FOR VEHICLE DAMAGE” and filed on Mar.24, 2019, the entire disclosures of which are hereby incorporated byreference herein.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to an image processing systemand method that automatically determines replacement parts and/or otheritems needed for and/or corresponding to repairing the vehicle, e.g., atFirst Notice of Loss (FNOL) or as desired.

BACKGROUND

When an insured vehicle is damaged and a vehicle insurance claim ismade, typically a representative of the insurance company or carrier(e.g., an adjustor, assessor, or other agent) assesses the damage andgenerates a preliminary identification of the replacement parts whichmay be necessary to repair the vehicle, and in some cases, an estimateof a settlement payment from the insurance company to a repair facilityfor repairing at least some portion of the damages to the vehicle usingthe identified, possible replacement parts, e.g., a “settlementestimate” at the insurance company/repair facility interface. Thispreliminary identification of possible replacement parts and estimatedsettlement is provided to or used by a particular repair facility thatis to perform the repair work. In many cases, upon performing its owninspection of the vehicle or upon tearing down the vehicle, theparticular repair facility finds additional damage that was notidentified in the initial assessment provided by the insurance carrier,as, for example, the repair facility is able to further access thevehicle and perform a more thorough examination than could an adjustorwho generally writes estimates based only on damages he or she can see,discern, or identify first-hand. When damages and/or costs that were notindicated in the preliminary assessment are discovered, the repairfacility may identify additional and/or alternate replacement parts thatare needed to repair the damaged vehicle, and may request an additionalmonetary amount or a “supplement” from the insurance carriercorresponding to the newly identified damages and/or costs.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In an embodiment, an image processing system includes a networkinterface via which a plurality of images of a damaged vehicle isreceived, and an image attribute extraction component that is configuredto generate, by operating on the plurality of images of the damagedvehicle, a set of image attributes that is indicative of a content of atleast some of the plurality of images of the damaged vehicle. The set ofimage attributes includes one or more image attribute types, andrespective values of the one or more image attribute types, for example.

The image processing system also includes one or more data storagedevices comprising non-transitory, tangible computer-readable mediastoring thereon historical claim data of a plurality of historicalvehicle insurance claims, and an access mechanism to the historicalclaim data. The historical claim data includes a plurality of claimattributes that include image attribute data of the plurality ofhistorical vehicle insurance claims, respective indications of actualreplacement parts of the plurality of historical vehicle insuranceclaims, and a plurality of other claim attributes of the plurality ofhistorical vehicle insurance claims.

Additionally, the system includes a parts identifier component that isconfigured to generate, based on the set of image attributes indicativeof the damaged vehicle, respective indications of one or morereplacement parts needed to repair the damaged vehicle. The partsidentifier component includes an information identification model thatis generated or created by accessing, via the access mechanism, thehistorical claim data of the plurality of historical vehicle insuranceclaims, and by performing a regression analysis on the accessedhistorical claim data to determine a subset of a plurality of imageattributes that are more strongly correlated to actual replacement partscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes.

The image processing system further includes an output interface viawhich the generated, respective indications of the one or morereplacement parts are provided to at least one of the network interfaceor a user interface.

In an embodiment, a method includes obtaining a plurality of images of adamaged vehicle and creating a set of image attributes of the obtainedimages, where the creation of the set of image attributes includes atleast one of detecting one or more features within the plurality ofimages, transforming at least one image of the plurality of images,filtering the at least one image or another image of the plurality ofimages, or calculating one or more metrics for at least one feature,color, texture, or contrast property within the plurality of images.Additionally, the method includes generating, based on the set of imageattributes and a stored information identification model, respectiveindications of one or more replacement parts that are needed to repairthe damaged vehicle, and providing the generated, respective indicationsof the one or more replacement parts needed to repair the damagedvehicle to a recipient. The stored information identification modelbased on which the respective indications of the one or more replacementparts are generated may be itself generated based on historical claimdata from a plurality of historical vehicle insurance claims, and mayinclude one or more independent variables corresponding to a set ofimage attributes that are more strongly correlated to replacement partsthan are other claim attributes of the historical claim data. At least aportion of the method may be performed by an image processing system,such as an embodiment of an image processing system described herein, oranother image processing system, for example.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for processing imagesof a damaged vehicle to determine or identify information correspondingto repairing the damaged vehicle, such as replacement parts and/or otheritems or information;

FIG. 2 is an example data flow in an exemplary image processing systemconfigured to determine or predict, by using an informationidentification or prediction model, information corresponding torepairing a damaged vehicle, such as needed replacement parts; and

FIG. 3 illustrates an example method of predicting or determininginformation corresponding to repairing a damaged vehicle based on imageattributes corresponding to one or more images of damage to the vehicle.

DETAILED DESCRIPTION

Although certain methods, apparatus, and articles of manufacture havebeen described herein, the scope of coverage of this patent is notlimited thereto. To the contrary, this patent covers all methods,apparatus, and articles of manufacture fairly falling within the scopeof the appended claims either literally or under the doctrine ofequivalents. As used herein, the term “vehicle” may include a car, anautomobile, a motorcycle, a truck, a recreational vehicle, a van, a bus,a boat or other amphibious vessel, heavy equipment, or any otherinsurable mode of transportation.

FIG. 1 is a block diagram of an exemplary system 100 for processingimages of a damaged vehicle to predict, determine, identify, and/orgenerate vehicle insurance claim information, such as replacement partsand/or other items or information corresponding to repairing the damagedvehicle. In an embodiment, the predicted, determined, identified, and/orgenerated information responding to repairing the damage to the vehiclemay be generated or created during the process of resolving an insuranceclaim for the vehicle, or as or when desired (e.g., without beingassociated with any insurance claim). As such, “vehicle insurance claiminformation” or “vehicle claim information,” as interchangeably usedherein, generally refers to information that is or may be generatedduring and at the final resolution of a vehicle claims resolutionprocess. For example, vehicle claim information may include indicationsof replacement parts that are needed to repair the vehicle as well asother resources are needed for otherwise utilized in the process ofrepairing vehicle, such as types of labor, labor costs, paint costs,towing costs, hazardous waste disposal costs, and the like.

Other examples of vehicle claim information include one or more types ofamounts (e.g., monetary amounts expressed in units of currency or as apercentage) that may be generated at various stages of resolving avehicle insurance claim. An example of a type of amount that may bepredicted by the system 100 is a settlement amount between a repairfacility and an insurance carrier providing the insurance policycovering the damaged vehicle, where the settlement amount is a monetaryamount to be paid by the insurance carrier to the repair facility forrepairing at least some of the damage to the vehicle. Another example ofa type of amount that may be predicted, determined, and/or generated bythe system 100 is a supplement amount to an estimate of a settlementbetween the insurance carrier and the repair facility. In this example,the supplement amount is an additional monetary amount corresponding toadditional costs of repairing the vehicle that were not indicated withthe estimate of the settlement. The settlement estimate may have beenautomatically predicted or provided by the system 100, or the settlementestimate may have been provided by another source. The supplement amountmay be expressed in units of currency, for example, or the supplementamount may be expressed as a percentage of the settlement estimate. Yetother examples of types of vehicle insurance claim amounts which may bepredicted by the system 100 include a settlement amount between theinsurance carrier and an insured party and a cost of parts and/or oflabor needed to repair the vehicle, e.g., to a defined level of quality.Generally, any one or more monetary amounts associated with resolving avehicle insurance claim may be predicted, determined, identified, and/orgenerated by the system 100.

Vehicle claim information that is able to be predicted, determined,identified, and/or generated by the system 100 may additionally oralternatively include one or more types of scores that are generatedduring the process of resolving and finalizing a vehicle insuranceclaim. An example of a score that may be predicted by the system 100 isa supplement score indicative of a probability of an occurrence of anon-zero supplement amount for the vehicle insurance claim. Otherexamples of predictable scores include a re-inspection score indicativeof a probability of an occurrence of a re-inspection of any or allportions of the vehicle insurance claim, and a customer serviceindicators (CSI) or customer satisfaction score provided by a customer(e.g., an insured party) during any step of the claim resolutionprocess. Generally, any type of score associated with a probability ofan event occurring during the resolution process of a vehicle insuranceclaim and/or associated with customer feedback generated during theresolution process of a vehicle insurance claim may be predicted,determined, and/or generated by the system 100.

The system 100 for processing one or more images of a damaged vehicleand predicting, determining, identifying, and/or generating vehicleinsurance claim information (such as replacement parts, other items,and/or other information corresponding to repairing the damaged vehicle)based on the processed images includes a particularly configured andparticularly connected computing device 102, which for the sake ofillustrating the principles described herein is shown in a simplifiedblock diagram of a computer. However, such principles apply equally toother particularly configured and particularly connected electronicdevices, including, but not limited to, cellular telephones, personaldigital assistants, wireless devices, laptops, cameras, tablets, smartphones or devices, media players, appliances, gaming systems,entertainment systems, set top boxes, and automotive dashboardelectronics, to name a few. In some embodiments, the computing device102 may be a server or a network of computing devices, such as a public,private, peer-to-peer, cloud computing or other known network.

The computing device 102 includes at least one processor 105 and atleast one non-transitory, tangible computer-readable storage media ordevice 108, such as a memory. The computing device 102 may be a singlecomputing device 102, or may be a plurality of networked computingdevices. In some cases, the computing device 102 is associated with aninsurance carrier. In some cases, the computing device 102 is associatedwith a repair facility. In some cases, the computing device 102 isassociated with a third party that is not an insurance carrier (e.g.,does not directly sell or issue insurance policies) and that is not arepair facility (e.g., does not perform vehicle repairs), and may or maynot be in communicative connection with a computing device associatedwith the insurance carrier and/or with a computing device associatedwith a repair facility.

As shown in FIG. 1, the computing device 102 is operatively connected toa data storage device 110 via a link 112. The data storage device 110may comprise non-transitory, tangible computer-readable storage media,such as, but not limited to RAM (Random Access Memory), ROM (Read OnlyMemory), EEPROM (Electrically Erasable Programmable Read-Only Memory),flash memory or other memory technology, CD (Compact Disc)-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,biological memories or data storage devices, or any other medium whichcan be used to store desired information and which can be accessed. Thedata storage device 110 may be a single storage device, or may be one ormore networked data storage devices having a unitary logical appearance,such as a databank, cloud storage, or other physical, non-transitorydata storage media. Although FIG. 1 illustrates the data storage device110 as being separate from the computing device 102, in some embodimentsthe data storage entity 110 may be contained within the same physicalentity as the computing device 102. The link 112 may be as simple as amemory access function, or it may be a wired, wireless, or multi-stageconnection through a network. Many types of links are known in the artof networking and may be contemplated for use in the system 100.

The data storage device 110 includes or stores claim data 111, such asclaim data related to historical vehicle insurance claims from one ormore insurance companies or carriers, and/or from other sources such asrepair shops, body shops, accident report databases, etc. Each datapoint in the claim data 111 corresponds to a particular historicalvehicle insurance claim and includes one or more types of information113, 114 corresponding to the claim, such as replacement parts, labortypes and labor costs, a final claim settlement amount, amounts and/orscores generated during various stages of claims resolution process,vehicle owner or insured information, information regarding the accidentor incident resulting in the claim, vehicle attribute information (e.g.,make, model, odometer reading, etc.), towing costs, hazardous materialdisposal costs, paint costs, tire costs, labor hours, and/or otherinformation corresponding to repairing the subject damaged vehicle. Thedifferent types of information or data 113, 114 that are stored for avehicle insurance claim are generally referred to interchangeably hereinas “vehicle insurance claim attributes,” “vehicle claim attributes,”“vehicle claim parameters,” “claim attributes,” “claim parameters,” or“claim data types.”

One type of claim attribute that is included in at least some of theclaim data points 111 is image data 114 of one or more images (e.g.,digital images) of vehicle damage corresponding to at least some of thehistorical vehicle insurance claims from one or more insurance companiesor carriers. For example, the image data 114 includes historical imagesof vehicle damage related to a plurality of vehicle insurance claims ofa plurality of insurance companies or carriers. The historical imagesmay be captured by an adjuster traveling to a damaged vehicle, whereverit is located, and documenting the damage to the vehicle with images orphotographs of the vehicle and, in particular, of the damaged portionsof the vehicle. In an implementation, an adjuster, customer, or otherparty may utilize a vehicle image capture and labeling system to capturevehicle photos, pictures, and/or images that are automatically labeled,i.e., annotated, such as described in co-pending U.S. patent applicationSer. No. 14/052,629 entitled “IMAGE CAPTURING AND AUTOMATIC LABELINGSYSTEM,” and filed on Oct. 11, 2013, the entire disclosure of which ishereby incorporated by reference herein.

In some implementations, the image data 114 may additionally oralternatively include image attribute vectors or other suitable format(e.g., arrays, data file, etc.) for representing the content within orof images of damaged vehicles, rather than, or in addition to, storingthe images themselves. “Content” of images may include, for example, atarget subject of an image (e.g., a damaged vehicle), one or moreobjects depicted in an image (e.g., portions of a vehicle orsurroundings of a vehicle), or any other visual representations withinan image. For ease of discussion, such visual representations within animage are generally referred to herein as the “content” of an image. Assuch, generally speaking, while an image attribute vector may beindicative and/or descriptive of at least some of the content includedwithin the actual images, the image attribute vector may require lessstorage space than the space needed to store the actual image contentthat the image attribute vector represents. Additionally, it is notedthat for ease of discussion herein, such compressed or smallerrepresentations of image content are generally referred to as “imageattribute vectors,” although it is understood that formats other thanvectors may be utilized to store data indicative of the content ofimages (e.g., image attribute types and their corresponding valueswithin the images). For example, other formats for storing image data114 may include arrays, data files, metadata, and the like, and theimage data 114 may include one or more other formats. Further, some orall of the techniques discussed herein may apply to such other formatsfor storing image data 114.

An image attribute vector may include a plurality of image attributevalues (e.g., discrete or continuous numerical values) for a pluralityof image attribute types that are indicative of at least a portion ofthe content of an image. By way of example and without limitation, imageattribute vectors may include image attribute types corresponding toindications of location of image features (e.g., point, edge, corner, orridge features), dimensions of image features (e.g., length, width, orradius), colors of points or areas in an image, textures of points orareas in an image, entropies of an image, contrasts of an image,lighting orientations, locations or properties of shadows in an image,etc.

In some implementations, the image data 114 may include image attributevalues separate from (e.g., that are not included in) any imageattribute vector. For example, the image data 114 may include a recordof a color image attribute that is representative of the color of atleast a part of the content of an image, where the value of the colorimage attribute is not included in any image attribute vector, e.g., isseparate from any other image attributes forming an image attributevector. Further, it is clear that the image data 114 may include anysuitable combination of images, image attribute vectors, and imageattribute values indicative of one or more historical images of damageto a vehicle.

In addition to image data 114, data in the claim data 111 correspondingto a particular historical vehicle insurance claim may also include aplurality of other claim properties or attribute types/values 113, suchas a final claim settlement amount, other amounts and/or scoresassociated with the claim, vehicle owner or insured information,information regarding the accident or incident resulting in the claim,and vehicle information (e.g., make, model, odometer reading, etc.). Theplurality of other claim properties 113 may also include customerservice indicators (CSIs) or customer satisfaction scores of arespective vehicle insurance claim. A claim data point corresponding toa particular vehicle insurance claim may include one CSI associated withthe claim, or may include multiple CSIs indicative of multiple feedbackpoints during the claims resolution process, e.g., a CSI for the overallclaims resolution process, another CSI for the repair facility, aanother CSI for the insurance carrier interface, etc. CSIs may be scaledacross the claim data 111.

Further, each particular data point of the historical claim data 111 maycorrespond to a partial or a total loss claim. For a partial loss claim,typically the vehicle was repaired by one or more repair facilities, andthus the corresponding data point may include claim attributes orproperties corresponding to an initial repair estimate, a finalsettlement amount between the insurance company and one of the repairfacilities, types and costs of replacement parts, labor costs, alocation of the repair facility, and the like. Other types of claimattributes or properties that may be included for a partial loss claimare an indication as to whether or not a supplement was generated forthe claim, and if a supplement was generated, the monetary amount of thesupplement. The claim data point may include one or more claimattributes corresponding to an indication of whether or not are-inspection occurred for the claim, and if a re-inspection did occur,the cost of performing the re-inspection (e.g., cost to the insurancecarrier and/or cost to the repair facility). Additionally, the claimdata point may include one or more claim attributes or propertiescorresponding to/indicative of the differential between an estimate thatoccurred after the re-inspection and an estimate performed prior to there-inspection (e.g., an estimate performed at First Notice of Loss(FNOL) or other estimate). For a total loss claim, such as when avehicle was stolen or was totaled, the corresponding data point mayinclude claim attributes or properties corresponding to/indicative of alocation of vehicle loss and an amount of a payment from the insurancecarrier to the insured.

A list of types of claim data information, parameters, attributes, orproperties, that may be included in the claim follows:

-   Insurance policy number-   Insurance company or carrier holding the insurance policy-   Identification of insured party-   Vehicle owner name; street, city and state address; zip code-   Location (e.g., state and zip code) where vehicle loss occurred-   Zip code where vehicle is garaged-   Vehicle driver name; age; street, city and state address; zip code-   Vehicle Identification Number (VIN)-   Vehicle make, model, model year, country of origin, manufacturer-   Vehicle type or body style (e.g., sedan, coupe, pick-up, SUV, wagon,    van, hatchback, convertible, etc.)-   Vehicle odometer reading-   Vehicle engine size, color, number of doors-   Whether or not the vehicle is leased-   Age of vehicle-   Condition of vehicle-   Settlement amount between insurance company and repair facility-   Payout amount (if any) to insured party or party holding the    insurance policy-   Loss date-   Vehicle appraisal inspection location and responsible adjustor-   Primary and secondary point of impact-   Vehicle drivable condition-   Airbag deploy condition-   Qualitative severity of damage-   Quantitative severity of damage-   Velocity of vehicle just prior to impact-   Change in velocity of vehicle due to impact-   Vehicle dimension score-   Vehicle repair score-   Initial estimate-   Estimate or prediction of settlement at FNOL-   Estimate from another repair facility or party-   One or more additional estimates and indications of when during the    claim settlement process the additional estimates occurred-   Occurrence of one or more re-inspections-   Cost to perform each re-inspection-   Revised estimate after re-inspection and corresponding repair    work/parts-   Occurrence of one or more supplements paid from insurance company to    repair facility-   Monetary amount of each supplement-   Level of desired target quality of repair-   Level of actual quality of repair-   Deductible-   Towing and storage costs-   Labor hours and costs for replacement and/or repair, and-   Type of labor (e.g., sheet metal, mechanical, refinish, frame,    paint, structural, diagnostic, electrical, glass, etc.)-   Type of replacement part (e.g., OEM (Original Equipment    Manufactured), new, recycled, reconditioned, etc.)-   Cost of replacement part-   Paint costs-   Tire costs-   Hazardous waste disposal costs-   Repair facility name, location, state, zip code-   Drivability indicator

As illustrated in the above, some of the claim attributes, parameters,or properties 113, 114 of claim data points 111 are vehicle parametersthat are indicative of a vehicle. Some claim attributes, parameters, orproperties 113, 114, are indicative of a driver, an owner, or an insuredparty of the vehicle, and some claim parameters or properties 113, 114may pertain to the insurance policy itself and/or to the resolution ofthe claim. Some claim attributes, parameters, or properties 113, 114 areindicative of the impact, collision or damage-causing incident. It isunderstood that not every vehicle claim in the claim data 111 isrequired to include every claim attribute, parameter, or property in thelist above. Indeed, some data points or vehicle claims in the claim data111 may include claim attributes, parameters, or properties that are noton the list.

Turning back to FIG. 1, the memory 108 of the computing device 102comprises non-transitory, tangible computer-readable storage media, suchas, but not limited to RAM (Random Access Memory), ROM (Read OnlyMemory), EEPROM (Electrically Erasable Programmable Read-Only Memory),flash memory or other memory technology, CD (Compact Disc)-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,biological memories or data storage devices, or any other medium whichcan be used to store desired information and which can be accessed bythe processor 105. In some embodiments, the memory 108 comprises morethan one computer-readable storage media device and/or device type.

The memory 108 includes computer-executable instructions 115 storedthereon for determining an information prediction model 118, which isalso referred to interchangeably herein as an information identificationmodel 118, information model 118, or model 118. Generally, theinformation model 118 includes one or more independent variables, one ormore dependent variables, and one or more mappings between values of theone or more independent and dependent variables. In the system 100, theone or more independent variables that are input into the informationprediction model 118 to determine values of the dependent variablesinclude at least some image attributes corresponding to images of asubject vehicle, and optionally may include other vehicle claimattributes, parameters, or properties. The dependent variables of theinformation prediction model 118 may include one or more variablescorresponding to predictions, determinations, identifications, and/orgenerations of indications of replacement parts, materials, other items,labor types, labor costs, amounts, scores, and/or other information thatmay be generated during a claims resolution process for a vehicleinsurance claim, and/or other information corresponding to repairing asubject damaged vehicle.

To determine the information prediction model 118 including the one ormore independent variables, the one or more dependent variables, and theone or more mappings between dependent and independent variablesincluded therein, the computer-executable instructions 115 includeinstructions for obtaining claim data 111 corresponding to a pluralityof historical vehicle insurance claims (e.g., vehicle insurance claimsthat have been made and settled) from the data storage device 110. Thehistorical claim data 111 includes at least some of the parameters,attributes, and/or claim properties listed above, and/or may includeother claim parameters, attributes, and/or properties. In particular,the historical claim data 111 may include, for a plurality of historicalvehicle claims, image data, such as digital images, image attributevectors, and/or image attribute values, actual vehicle claim information(e.g., actual amounts and/or scores for historical vehicle insuranceclaims), and a plurality of other claim attributes, parameters, and/orproperties. Obtaining the claim data 111 from the data storage device110 may include performing a database read or some other database accessfunction, or may include initiating a message exchange between thecomputing device 102 and the data storage device 110, or some otheraccess mechanism. In some embodiments, obtaining the claim data 111 mayinclude obtaining all claim attribute, parameter, and/or property valuesfor a particular historical vehicle insurance claim. In someembodiments, obtaining the claim data may include obtaining a subset ofall attribute, parameter, and/or claim property values that areavailable for the particular historical vehicle insurance claim.

The computer-executable instructions 115 for determining the informationmodel 118 include instructions for performing a data analysis on theobtained claim data 111 to determine a subset of the plurality of claimattributes, parameters, and/properties and/or image attributes that aremost closely correlated to vehicle claim information (e.g., toreplacement parts, other items, labor types, labor costs, paint costs,replacement parts types, amounts, scores, costs, other information thatis or may be generated during the claims resolution process, and/orother information that is needed for and/or corresponds to repairing adamaged vehicle) across the claim data 111, for example. In some cases,the types of vehicle claim information that are desired to bedetermined, identified, generated, and/or predicted may be selected orotherwise indicated. A single type of vehicle claim information may beselected or indicated, or multiple types of vehicle claim informationthat is desired to be determined, identified, generated, and/orpredicted may be selected or indicated. Additionally, the data analysisperformed on the obtained claim data 111 may be, for example, a linearregression analysis, a multivariate regression analysis such as theOrdinary Least Squares algorithm, a logistic regression analysis, a K-thnearest neighbor (k-NN) analysis, a K-means analysis, a Naïve Bayesanalysis, another suitable or desired predictive data analysis, one ormore machine learning algorithms, or some combination thereof.

The subset of the plurality of attributes or parameters that are mostclosely correlated to the desired type(s) of actual vehicle claiminformation across the claim data 111 are identified as the independentvariables of the information model 118. In an embodiment, at least someimage attribute types are independent variables of the informationprediction model 118. For example, the data analysis may determine thatthe certain types or locations of image features are independentvariables of the model 118 (e.g., location of one or more edges in animage, radius of a point feature in an image, or color of one or moreareas within an image). Additionally or alternatively, in somesituations, the data analysis may determine that one or more other claimattributes or properties are independent variables of the informationmodel 118 (e.g., a year of manufacture of the subject vehicle, or alength of time that the policy has been in force).

In some embodiments, the instructions 115 for determining an informationmodel 118 include instructions for determining a weighting ofindependent variables commensurate with the strength of their respectivecorrelation to one or more dependent variables. In these embodiments,the instructions 115 identify, determine, generate, and/or predict thevehicle claim information based on the weighting of values of theindependent variables for the particular vehicle insurance claim. Forexample, to predict a customer service indicator score, the instructions120 may give priority to fitting a detected image attribute indicativeof an edge feature to independent variable values (or ranges thereof)over fitting a detected image attribute indicative of color, if an edgefeature is found (via the data analysis) to be more strongly correlatedto the CSI than is a color image attribute.

A total number of independent variables of the model 118 may beconfigurable or selectable. For example, the total number of independentvariables may be limited to include only parameters that have at-statistic greater than a certain threshold, where the t-statistic is ameasure of how strongly a particular independent variable (e.g., imageattribute) explains variations in a dependent variable. Additionally oralternatively, the total number of independent variables may be limitedto include parameters that have a P-value lower than another threshold,where the P-value corresponds to a probability that a given independentvariable is statistically unrelated to a dependent variable.

Still further, the total number of independent variables of the model118 may be additionally or alternatively limited based on anF-statistic, where the F-statistic evaluates an overall statisticalquality of the information prediction model 118 with multipleindependent variables. For example, all of the determined independentvariables may be initially included in the information prediction model118, and those independent variables with lower t-statistics may begradually eliminated until the F-statistic for the informationprediction model 118 increases to a desired level. Of course, the numberof independent variables may be additionally or alternatively configuredbased on other statistical or non-statistical criteria as well, such asuser input.

The computer-executable instructions 115 include instructions fordetermining the one or more mappings between values of the independentvariables and the dependent variables of the information predictionmodel 118. For example, values (or ranges thereof) of the parameters orattributes determined to be independent variables may be mapped tovalues (or ranges thereof) of predicted amounts or scores of a vehicleinsurance claim. In some embodiments, different values or ranges ofvalues of the independent variables may be grouped or segmented formanageability purposes.

In some embodiments of the system 100, the instructions 115 fordetermining the information prediction model 118 include instructionsfor performing a cluster analysis on the claim data 111 prior toperforming the data analysis. A cluster analysis may be performed towhittle the plethora of candidate independent variables representedwithin the claim data 111 down to a manageable or desired number ofclusters, so that a similarity between data points (e.g., particularimages, image attribute vectors, image attribute values, and/or claimproperties) within a cluster is maximized and a similarity betweenvarious clusters is minimized. For example, a clustering of vehicleinsurance claims based on image attribute vector similarities may beperformed, resulting in sets of vehicle insurance claims clusters havingsimilar image attribute vectors (e.g., similar detected features,colors, and textures) or having similar image attribute patterns (e.g.,similar ratios or correlations between image attribute values). Inanother example, a cluster analysis of claims included in the claim data111 based on an average actual settlement cost may be performed,resulting in a set of clusters of vehicle insurance claims where theclaims in each cluster are most closely interrelated based on averageactual settlement cost. Other example of clustering based on other claimproperties may be possible. The cluster analysis may be performed by anyknown clustering algorithm or method, such as hierarchical clustering,disjoint clustering, the Greenacre method (e.g., as described inGreenacre, M. J. (1988), “Clustering Rows and Columns of a ContingencyTable,” Journal of Classification, 5, pp. 39-51), or portions,variations or combinations thereof.

The number of clusters obtained from a cluster analysis may beconfigurable or selectable. For example, a desired number of clustersmay be based on user input. Additionally or alternatively, the desirednumber of clusters may be based on a desired level of similarity ordissimilarity between clusters. Other bases for configuring the numberof clusters are also possible.

After the information prediction model 118 (including independentvariables, dependent variables, and mappings) is determined by theinstructions 115, the computing device 102 may store the informationprediction model 118 in the memory 108. Alternatively or additionally,the computing device 102 may store some or all portions of theinformation prediction model 118 in the data storage device 110.

In FIG. 1, the memory 108 includes further computer-executableinstructions 120 stored thereon for receiving a request to predict,identify, generate, and/or determine vehicle claim attribute informationfor a particular vehicle. For example, the particular vehicle may beassociated with a particular insurance claim for the particular vehicle,e.g., for a new claim or for a claim that is not included in thehistorical claim data 111. In another example, the particular vehiclemay not be associated with any vehicle insurance claim, such as when avehicle owner merely desires to repair the vehicle without informing hisor her insurance carrier. At any rate, in an embodiment, thecomputer-executable instructions 120 may be configured to receive andprocess the request to predict, identify, determine, and/or generaterespective indications of one or more replacement parts, materials,other items, labor types, labor costs, and/or other values of parameterscorresponding to repairing a subject damaged vehicle, which may or maynot correspond to a vehicle insurance claim. In some embodiments (notshown), the computer-executable instructions 115 and 120 may both beincluded in a single set of instructions, but in FIG. 1 they are shownas separate entities 115, 120 for clarity of discussion.

Additionally, in FIG. 1, the requesting entity is illustrated as being arequesting computing device 122, but this is only exemplary, as therequesting entity may be another type of entity such as a human whointeracts with the system 100 via a local or remote user interface or anapplication executing on the computing device 102, the computing device122, or another computing device. In FIG. 1, the requesting computingdevice 122 is communicatively coupled to the computing device 102 via anetwork 125. The network 125 may be, for example, a private local areanetwork, a wide area network, a peer-to-peer network, a cloud computingnetwork, the Internet, a wired or wireless network, or any combinationof one or more known public and/or private networks that enablecommunication between the computing devices 122 and 102. In someembodiments, the network 125 may be omitted, such as when the computingdevice 122 and the computing device 102 are directly connected or are anintegral computing device.

In some scenarios, the requesting computing device 122 may be a tablet,laptop, smart device, server, or other computing device that isassociated with, owned or operated by the insurance company. Forexample, the requesting computing device 122 may be a tablet, laptop, orsmart device used by a field assessor while the assessor is at a fieldsite inspecting vehicle damage, e.g., at FNOL. In another example, therequesting computing device 122 may be a back-end computing server ornetwork of computing devices of the insurance company that processes allincoming claims, or the requesting computing device 122 may be a host ofa website that agents of the insurance company are able to access via abrowser.

In some scenarios, the requesting computing device 122 may be a tablet,laptop, smart device, server, or other computing device associated with,owned or operated by the repair facility. For example, the requestingcomputing device 122 may be a tablet, laptop, smart device, or desktopcomputing device located at the repair facility that is used to trackcustomers and repairs to their vehicles. In another example, therequesting computing device 122 may be a computing server or network ofcomputing devices at a back-end office of an owning company of therepair facility that processes vehicle repair work orders related toinsurance claims for many repair facilities.

Returning to the memory 108, the further computer-executableinstructions 120 stored thereon are executable to receive the requestfor predicting, identifying, determining, and/or generating vehicleclaim information, such as replacement parts and/or other information.In some scenarios, the request includes an indication of a type ofvehicle claim information that is requested or desired to be predicted,identified, determined, and/or generated, e.g., replacement parts,items, and/or other resources needed to repair the subject vehicle, asettlement amount between the insurance carrier and a repair facility, asettlement amount between the insurance carrier and an insured party, aprobability of a re-inspection, a CSI score, other types of amounts,scores, and/or other information that may be generated during a claimsresolution process for vehicle insurance claim, and/or otherinformation/values of parameters corresponding to repairing the subjectvehicle, such as those discussed above.

In some scenarios, the request received at the system 100 includes orindicates one or more images of the damaged vehicle, which may beindicative or descriptive of the damage to the vehicle. That is, thecontent of the images depicts damage to the vehicle via target subjectsof the images, objects depicted in the images, etc. The images of thedamage to the vehicle may be represented in any suitable bitmap orvector format, such as Joint Photographic Experts Group (JPEG), GraphicsInterchange Format (GIF), Portable Networks Graphics (PNG), PortableDocument Format (PDF), or Encapsulated Postscript (EPS), for example.

The computing device 102 may execute the computer-executableinstructions 120 to extract, determine, create, and/or or generate oneor more image attribute vectors and/or image attribute values based onand corresponding to images indicated by or included with the request.The creation of one or more image attribute vectors and/or values forthe received images may include an application of any suitablecombination of feature detection techniques, filters, transformations,statistical measure calculations, etc., as further discussed withreference to FIG. 2 below.

Image attribute vectors (e.g., generated via execution of thecomputer-executable instructions 120) may, in an implementation, includea plurality of numerical, symbolic, and/or textual values correspondingto a plurality of types of image attributes (e.g., image attributevalues). An example image attribute vector may include one or morenumeric values indicative of locations and/or dimensions of features(edges, corners, points, etc.) within an image and one or more textualand numeric indications of colors, contrasts, and textures within animage (statistical measures of color, contrast, and texture within animage). In this manner, an image attribute vector may be indicative ofthe content of an image, while remaining less data, memory, and/orprocessing intensive than the image itself.

In some implementations, an adjuster or computing device may tag orlabel the images of damage to the vehicle when the images are captured,such that a computing device or individual examining the images may moreeasily deduce certain image information. The image information mayinclude, by way of example, portions of the vehicle included in an image(passenger-side left, front, back, drivers-side back, etc.), a timestamprepresenting when the image was captured, an identification of thevehicle (license plate, insurance claim policy number, VIN number,etc.). In some cases, such tags, labels, or other annotations may beincluded in an image attribute vector.

Additionally, in some cases, the request for the vehicle claiminformation includes one or more values corresponding to one or moreother claim attributes, parameters, and/or properties of vehicleinsurance claims, e.g., parameters corresponding to the insurance policycovering the damaged vehicle (e.g., deductible, identifications ofauthorized repair facilities, etc.); data specific to the particularvehicle, such as a VIN (Vehicle Identification Number); a desired levelof repair quality; and/or other data indicative of attributes of vehicleinsurance claims. One or more of these other received claim attribute,parameter, and/or property values may be utilized to predict, identify,determine, and/or generate the requested vehicle claim information, ornone of the other received claim attribute, parameter, and/or propertyvalues may be utilized.

Furthermore, it is noted that the request received by the system 100 maytake any known form, such as a message, a data transfer, a web-servicecall, or some other action taken by an application or function.

From the specific claim and image data included in or indicated by therequest, the instructions 120 determine the values for some or all ofthe independent variables of the information prediction model 118, andprovide at least some of the values as inputs to the informationprediction model 118. When a request does not reference valid values forall independent variables of the information prediction model 118, theinstructions 120 may attempt to provide a best fit. For example, theinstructions 120 may ignore independent variables for which no or aninvalid value was provided in the request, or the instructions 120 mayassign a default value for those independent variables. In some cases,particular image attribute vectors, values or claim properties may beprovided as inputs to the information prediction model 118 irrespectiveof whether or not they are independent variables of the informationprediction model 118.

The computer-executable instructions 120 may determine, predict,identify, and/or generate vehicle claim information (e.g., one or morereplacement parts, labor types in cost, amounts, scores, other itemsand/or information that is or may be generated by resolving a vehicleinsurance claim, and/or other information corresponding to repairing thesubject vehicle) based on: (i) image attribute types and theirrespective values (which may or may not be included in a request), (ii)mappings of the information prediction model 118; and, optionally insome cases, (iii) values of other claim attributes, parameters, and/orproperties (which may or may not be included in a request). For example,the computer-executable instructions 120 may cause one or more imageattribute values and one or more claim attribute, parameter, and/orproperty values corresponding to the subject vehicle to be input intothe information prediction model 118, which then may generate, as anoutput, one or more predicted or identified replacement parts, materialsor items, labor types and costs, amounts, scores, other vehicle claiminformation, and/or other informational resources corresponding torepairing the subject vehicle.

In some embodiments, the information prediction model 118 stored in thesystem 100 is trained or updated to account for additional data (e.g.,additional image attribute data) that has been added to the claim data111. Training or updating may be triggered periodically at a giveninterval, such as weekly, monthly or quarterly. Training or updating maybe triggered when a particular quantity of additional data points hasbeen added to the original claim data 111. In some embodiments, prior totraining, some portion of the original claim data 111 is deleted, suchas older labor cost data that no longer accurately reflects labor marketwages. Additionally or alternatively, training or updating may betriggered by a user request.

When a trigger to update the information model 118 is received by thesystem 100, the system 100 may perform some or all of the instructions115 to re-determine at least a portion of the information predictionmodel 118 based on the additional claim data or a new set of claim data.The re-determination may operate on only the additional claim data, ormay operate on an aggregation of one or more portions of the originalclaim data 111 and the additional claim data. The re-determination mayinclude repeating some or all of the steps originally used to determinethe original information prediction model 118 on the additional claimdata. For example, the re-determination may include performingpredictive analysis on the additional claim data to determine if theadditional claim data statistically supports revising the independentvariables of the information prediction model 118. In another example,the re-determination may include performing cluster analysis on theaggregation of the additional claim data and at least a portion of theoriginal claim data. The exact set of steps to be repeated on theadditional claim data may be selectable, and/or may vary based onfactors such as a quantity of additional data points, time elapsed sincethe last update, a user indication, or other factors. There-determination may result in an updated information prediction model118, which then may be stored in the system 100.

Note that the information prediction model 118 generated by the system100, and in particular, updates to the information prediction model 118may result in more accurate potential cost estimates over time. With thesystem 100, the information prediction model 118 may be continuallyupdated with additional claim data, thus ensuring a more statisticallyaccurate reflection of identities and values of independent variables,and accordingly, a more statistically accurate prediction of an amountof a supplement.

Additionally, although FIG. 1 illustrates both the instructions fordetermining an information prediction model 118 and the instructions forresponding to requests 120 being stored and executed by the samecomputing device 102, in some embodiments, the two sets of instructions115, 120 are stored on and executed by different computing devices orsystems that may be in communicative connection with each other.Further, in some scenarios, the computing device 102 is associated with,owned or operated by the insurance company that issued the policy underwhich the damaged vehicle is covered. For example, the computing device102 may be a back-end server or network of computing devices of theinsurance company that store and execute the instructions 120 forresponding to requests (e.g., from computing devices 122 of fieldassessors or agents), and may be in communicative connection withanother computing device (not shown) that stores and executes theinstructions 115 for determining the information prediction model 118.

In some scenarios, the computing device 102 is associated with, owned oroperated by the repair facility that is to repair the damaged vehicle.For example, the computing device 102 may be a back-end server ornetwork of computing devices of an owning parent company of the repairfacility that stores and execute the instructions 120 for responding torequests from multiple computing devices 122 of various repair facilitystore fronts or franchise locations, and may be in communicativeconnection with another computing device (not shown) that stores andexecutes the instructions 115 for determining the information predictionmodel 118.

In some scenarios, the computing device 102 is associated with, owned oroperated by a third party that is not the insurance company that issuedthe policy under which the damaged vehicle is covered, and is not one ofthe repair facilities that is to repair the vehicle damages. Forexample, the computing device 102 may be associated with a company ororganization that provides predictive products and resources to multipleinsurance companies, repair facilities, and other companies or entitiesassociated with repairing damages to insured vehicles.

FIG. 2 depicts an exemplary data flow in an embodiment of a system 150that includes a computing device 152 particularly configured to predict,identify, determine, and/or generate vehicle claim information (e.g.,replacement parts, replacement part types, labor types, labor costs,other costs, amounts, scores, other items, and/or other information thatis or that may be generated by the process of resolving a vehicleinsurance claim, and/or other information needed for and/orcorresponding to repairing a damaged vehicle) based, at least partially,on detected image attributes. The computing device 152 may be acomputing device with a memory, a processor, and particularcomputer-executable instructions 155, 170, and 172 stored on its memoryand executable by its processor. The computing device 152 may operate inconjunction with embodiments of the system 100 of FIG. 1, and in someembodiments, the computing device 152 may be the requesting computingdevice 122 of FIG. 1.

In FIG. 2, the computing device 152 of the system 150 includesinstructions 170 for obtaining plurality of images of the subjectvehicle, e.g., of damaged portions of the subject vehicle. The actualplurality of images of the subject vehicle may be included in a requestto predict vehicle claim information, in an implementation.Alternatively, image attributes and/or values indicative of the contentof the actual plurality of images may be included in a request topredict vehicle claim information. In other implementations, a requestdoes not include images or image attributes. Rather, the request mayinclude references or indices that direct (or point) the instructions170 to database records, or other data entries that store images orimage attributes (e.g., stored in the data storage device 165).

In embodiments in which a plurality of images of the subject vehicle isobtained by the computing device 152, the system 150 may includeinstructions 172 for performing image processing on the plurality ofimages to extract, determine, generate, and/or create one or more imageattribute vectors corresponding to the plurality of images andindicative of their content. The image attribute processing orextraction 172 may be performed by the computing device 152 (asindicated by reference 172 a), by the computing device 162 (as indicatedby reference 172 b), or by both computing devices 152, 162 operatingtogether in concert. In these embodiments in which a plurality of imagesof the subject vehicle is obtained by the computing device 152, theplurality of images of the subject vehicle are inputs into the imageattribute extraction 172, and a plurality of image attributes, or one ormore image attribute vectors, may be an output of the image attributeextraction 172.

When executed by one or more processors of the computing device 152 orthe computing device 162, the image attribute extraction instructions172 may perform a feature detection. Performing a feature detectionincludes performing any suitable feature detection or feature extractiontechnique or techniques such as an edge detection, corner detection,blob detection, ridge detection, scale-invariant feature transformation,thresholding, template matching, etc. The feature detection outputs aplurality of image attributes (e.g., expressible as an image attributevector), where the plurality of image attributes include one or morelocations of features within images, sizes or dimensions of featureswithin images, types of features within images, or intersections offeatures within images. In one example scenario, the feature detectiondetects a broken window or dent in a vehicle as a plurality of edges orridges in an images intersecting at one or more points. The plurality ofimage attributes may be output from the feature detection as a vector ofnumbers, characters, and/or symbols.

Also, when executed by one or more processors of the computing device152 or the computing device 162, the image attribute extractioninstructions 172 may apply filters or other transformations to an imageas a whole or to portions of an image to extract image attributes. In anexample, the image attribute extraction 172 applies Gaborfilters/functions, as known in the industry, to one or more images orportions of an image to extract scales, orientations, etc. from animage. Alternatively, the image attribute extraction 172 may utilize awavelet filter bank, wavelet transformation, or other filter bank toextract textures from an image or a portion of an image, or the imageattribute extraction 172 may transform the image into a binary color(e.g., black and white) image to detect edges or shapes within an image.In some cases, the image attribute extraction 172 applies filters,transformations, and/or other image processing techniques to detectlighting orientations or shadow locations/orientations within an image.Generally, an image attribute extraction may apply any suitable type offilters or transformations to either pre-process an image before furtherattribute extraction or to directly extract image attributes (e.g.,output as an image attribute vector).

Still further, when executed by one or more processors of the computingdevice 152 or the computing device 162, the image attribute extractioninstructions 172 may calculate statistical measures, gradient measures,or other metrics for an image as a whole or for portions of an image.For example, the image attribute extraction 172 may calculate afrequency of an attribute within an image, an average of an attributewithin an image, a distribution of an attribute within an image, etc.The image attribute extraction 172 may calculate an average, gradient,summed, or otherwise mathematically determined color, texture, orcontrast property within an image or a portion of an image, or the imageattribute extraction 172 may calculate a frequency or distribution(e.g., standard deviation, mean, and median) of pixels of a certaincolor within an image. In an embodiment, such statistical measures ormetrics are output as a plurality of image attributes (e.g., in the formof an image vector) from the image attribute extraction 172, where eachof the plurality of image attributes indicates a value of thestatistical measure or metric.

The instructions 172 may cause the computing device 152 to additionallyor alternatively apply or perform any one or more other suitable imageattribute extraction and/or image processing techniques, such as thosedescribed in commonly-owned U.S. Pat. No. 8,239,220 entitled “METHOD ANDAPPARATUS FOR OBTAINING PHOTOGRAMMETRIC DATA TO ESTIMATE IMPACTSEVERITY,” the entire disclosure of which is hereby incorporated byreference herein, and/or other image processing techniques.

Returning to FIG. 2, the instructions 155 stored on the computing device152 include instructions for obtaining values of image attribute vectorsand, in some cases, values of claim attributes, parameters, and/orproperties corresponding to a subject vehicle. The image attributevectors, or image attribute values, may be input to the instructions 155from the image attribute extraction 172 a or may be retrieved by theinstructions 155 from a data storage device record or entry indicated inthe request. The values of the claim attributes, parameters, and/orproperties may be obtained via a user interface, by reading from a file,by extracting from a message, or by any other known means of obtainingvalues. The obtained values may optionally include other valuescorresponding to any other claim parameter or combination of parameters,such as those included in the previously discussed list or other claimparameters.

In some embodiments, the instructions 155 additionally includeinstructions for obtaining a selection or indication of one or moreparticular types of vehicle claim information (e.g., one or moreparticular parts, materials, labor types, labor costs, amounts, scores,and/or other vehicle claim information) that is to be predicted,identified, determined, and/or generated based on the image attributescorresponding to the subject vehicle. For example, the selection ofvehicle information type may be indicated by a user interface, in amessage, by an indicator stored in a database, or by some othermechanism.

The instructions 155 in FIG. 2 further include instructions forobtaining, based on the values of the obtained image attribute vectorsand/or claim attributes or parameters, and based on the informationprediction model 158, predicted, determined, identified, and/orgenerated vehicle claim information (e.g., parts, materials, otheritems, costs, amounts, scores, and/or other information corresponding tothe subject damaged vehicle), as determined by an information predictionmodel 158. As shown in FIG. 2, the information identification model 158may be entirely stored at the computing device 162 (e.g., reference 158a), the information identification model 158 may be entirely stored at adata storage entity 165 that is accessible to the computing device 162(e.g., reference 158 b), or the information identification model 158 maybe stored across both the computing device 162 and the data storagedevice 165 (e.g., references 158 a and 158 b).

In the system 150, to obtain the indication of the predicted,identified, determined, and/or generated vehicle claim information, thecomputing device 152 requests 160 another computing device 162 that isparticularly configured to access an information prediction model 158 aand/or 158 b. The requesting 152 and the responding 162 computingdevices may be directly or remotely connected via one or more publicand/or private networks. In some embodiments of the system 150, therequesting computing device 152 and the responding computing device 162may have a client/server relationship. In some embodiments, thecomputing devices 152 and 162 may have a peer-to-peer or cloud computingrelationship, or the computing devices 152 and 162 may be an integralcomputing device. Other relationships between the computing devices 152and 162 are also possible. Thus, the request 160 may take any knownform, such as sending a message, transferring data, or performing aweb-service call.

In some embodiments, the request 160 includes values of image attributesdescriptive or indicative of the damage to the vehicle. The request 160may additionally or alternatively include values of one or more otherclaim attributes or properties corresponding to the damaged vehicle. Insome embodiments, the values of only the image attributes that have beendetermined to be independent variables of the information predictionmodel 158 a, 158 b are included in the request 160.

Upon receiving the request 160, the responding computing device 162determines, identifies, generates, and/or predicts the vehicle claiminformation (e.g., one or more particular parts, materials, labor types,labor costs, amounts, scores, and/or other vehicle claim information)based on the one or more image attribute values (and, in some cases,based on the claim attribute property values included in the request160) and the information prediction model 158 a, 158 b. For example, oneor more of the image attributes included in the request 160 are inputinto the information prediction model 158 a, 158 b. Similar to thesystem 100 of FIG. 1, if the request 160 omits or provides an invalidvalue for an image attribute or claim property that is an independentvariable of the information prediction model 158 a, 158 b, the computingdevice 162 may process the request 160 based on a best fit of theprovided values in the request 160. The responding computing device 162may return an indication 168 of the identified vehicle claim information(e.g., the one or more particular parts, materials, labor types, laborcosts, amounts, scores, and/or other vehicle claim information).

The requesting computing device 152 obtains the indication 168 of theidentified vehicle claim information from the responding computingdevice 162, and may cause the indication of the identified vehicle claiminformation to be presented at a user interface (e.g., of the requestingcomputing device 152 or of another computing device). In someembodiments, the requesting computing device 152 causes the indication168 of the identified vehicle claim information to be transmitted toanother computing device.

FIG. 3 is an embodiment of an example method 200 of predicting vehicleclaim information, such as replacement parts which are needed to repaira damaged vehicle, based on processing images of the damage to thevehicle. Embodiments of the method 200 may be used in conjunction withone or more of the systems of FIGS. 1 and 2, and with the previouslydiscussed list of possible claim attributes, properties, and/orparameters, and/or with other claim parameters. For example, the method200 may be performed by the computing device 102, the computing device152, and/or the computing device 162. For ease of discussion, and notfor limitation purposes, the method 200 is described with simultaneousreference to FIGS. 1 and 2, although the method 200 may be performed byor in conjunction with systems other than the system 100 of FIG. 1 andthe system 150 of FIG. 3.

The method 200 includes obtaining 202 one or more images of damage to avehicle. Typically, the plurality of images includes images of damagedportions of the vehicle, and the images may capture the vehicle fromdifferent angles. The plurality of images may be obtained (block 202) ata computing device 102 of a system 100 configured to predict, identify,determine, and/or generate vehicle claim information (e.g., one or morereplacement parts, labor types and costs, amounts, scores, other itemsand/or information that is or may be generated by resolving a vehicleinsurance claim, and/or other information corresponding to repairing asubject vehicle). The plurality of images may be obtained byelectronically receiving the images from another computing device, theplurality of images may be received via a camera interface of thecomputing device 102, or the computing device 102 may retrieve theplurality of images from a data storage area.

The plurality of images are operated on by the method 200 (e.g., areimage processed) to generate data used to predict, identify, generate,create, and/or determine one or more types of vehicle claim informationfor a damaged vehicle. (As previously discussed, although the term“vehicle claim information” is utilized herein, the method 200 may applyto damaged vehicles that are not associated with any insurance claim,e.g., when a vehicle owner desires to repair his or her vehicle withoutmaking an insurance claim.) The method 200 may process the obtainedimages to create image data used to predict one or more replacementparts, labor types and costs, amounts, other items and/or informationthat is or may be generated by resolving a vehicle insurance claim,and/or other information corresponding to repairing the subject vehicle.Additionally or alternatively, the method 200 may process the obtainedimages to determine image data used to predict a score corresponding tothe vehicle insurance claim (such as a customer satisfaction score or ascore indicative of a probability of an occurrence of an event duringthe claim resolution process, e.g., a re-inspection). In an embodiment,the method 200 includes receiving an indication of one or more types ofvehicle claim information that is desired to be predicted, identified,generated, and/or determined.

In an implementation, the method 200 includes generating an imageattribute vector corresponding to the one or more images andindicative/descriptive of content contained within the one or moreimages (block 205). For example, the computing device 102 itself mayperform an image attribute extraction, such as the image attributeextraction 172, or the computing device 102 may request anotherapplication, device or system to perform an image attribute extraction.Typically, the output of the image attribute extraction 172 includes animage attribute vector corresponding to each of the one or more images,which may include a plurality of image attribute types and theirrespective values, such as images attributes related to detectedfeatures, colors, textures, statistical measures, etc. within an image.

Further, the method 200 includes causing the image attribute vectorcorresponding to at least one image of the damaged vehicle to be inputinto or provided to an information prediction model (e.g., the model 158a, 158 b of FIG. 2) to predict vehicle claim information, such as one ormore replacement parts, labor types and costs, amounts, scores, otheritems and/or information that is or may be generated by resolving avehicle insurance claim, and/or other information corresponding torepairing the subject vehicle (block 210). The information predictionmodel may have been generated based on a data analysis performed onhistorical vehicle insurance claim data. As previously discussed, thedata analysis performed on the historical claim data may be, forexample, a linear regression analysis, a multivariate regressionanalysis such as the Ordinary Least Squares algorithm, a logisticregression analysis, a K-th nearest neighbor (k-NN) analysis, a K-meansanalysis, a Naïve Bayes analysis, another suitable or desired predictivedata analysis, one or more machine learning algorithms, or somecombination thereof. The historical vehicle insurance claim data mayinclude partial and total loss vehicle claim data obtained or collectedfrom one or more insurance companies and/or from other sources such asrepair shops, body shops, accident report databases, etc. Generally, theclaim data corresponds to vehicle insurance claims that have beenresolved, and includes claim data such as historical image attributetypes and their corresponding values, historical images of damage tovehicles, settlement estimates and corresponding repairs, supplementamounts and corresponding repairs, whether or not re-inspections wereperformed, final settlement amounts, date of claims, identification ofone or more repair facilities and their locations, a level of quality ofthe repairs, customer service indicator scores, actual values of othervehicle claims amounts and/or scores, any of the claim parameters in thepreviously discussed list, and/or other claim parameters.

The information prediction model (e.g., the information prediction model158 a, 158 b) is configured to generate or output a prediction,identification, or generation of vehicle claim information based on theimage attribute vector and optionally based on one or more claimattributes, parameters, and/or properties corresponding to the damagedvehicle that are input into the model. The inputs into the informationprediction model include at least some of the image attributes in animage attribute vector corresponding to an image of the damaged vehicle,and optionally one or more values of one or more claim attributes,parameters, and/or properties that were determined, by the dataanalysis, to be more strongly correlated to a magnitude of a desiredamount or score for the vehicle insurance claim than are other claimattributes, parameters, and/or properties. Based on the inputs (e.g.,independent variables of the model) and one or more mappings included inthe model, the model determines one or more outputs (e.g., dependentvariables of the model), including the predicted, identified,determined, generated, and/or created vehicle claim information.

The method 200 includes obtaining or receiving the predicted,identified, determined, generated, and/or created vehicle claiminformation from the information prediction model (block 212), andindications of at least some of the predicted information may beprovided to a user interface and/or to a recipient computing device(block 215). For example, at least some of the indications are providedto a user interface of the computing device 102 or to a remote userinterface (e.g., via a web portal), and/or at least some of theindications are transmitted to another computing device (e.g., acomputing device associated with the repair facility or a back-endsystem of an insurance carrier). Typically, the indication(s) of thepredicted vehicle claim information is provided to the user interfaceand/or to another computing device 215 at an early stage of the claimresolution process, e.g., at FNOL. Indeed, in some embodiments, theentirety of the method 200 is performed at FNOL, e.g., by a computingdevice or tablet of a field assessor, or by a field computing device ortablet in conjunction of a back-end system.

In an embodiment in which one or more replacement parts are determinedvia the block 212, the method 200 may include ordering the one or morereplacement parts and/or repairing the damage vehicle using the one ormore replacement parts.

The techniques, systems, methods and apparatuses described herein forutilizing an image processing system for vehicle damage (e.g., toidentify, predict, or determine parts needed to repair a damaged vehicleas well as identify, predict, or determine other vehicle claiminformation, such as amounts, scores, and/or other information), allowthe replacement parts for the damaged vehicle to be identified (and insome cases, ordered) based on images of the damaged vehicle. Indeed, insome embodiments, the techniques, systems, methods and apparatuses ofthe present application allow the identification, prediction, ordetermination of parts needed to repair the damaged vehicle (as well asother vehicle claim information) to be determined solely based on theimages of the damaged vehicle. For example, the identification,prediction, or determination of parts needed to repair the damagevehicle (and/or other vehicle claim information) may be performedwithout any user input other than that of providing the images. Inparticular, a user is not required to identify, label, or otherwiseprovide any other information along with the images of the damagedvehicle in order to be informed of the parts needed to repair thevehicle. Indeed, in some embodiments, the method 200 may be entirelyautomatically performed without any user input and/or interaction atall, aside from obtaining the images of the damaged vehicle (block 202).As such, in an example scenario, the method 200 may be performed inreal-time at FNOL of an insurance claim, thereby allowing the identifiedparts to be ordered at FNOL. In another example scenario, if the ownerof a damaged vehicle decides not to file an insurance claim, the ownermay still be able to accurately obtain the identification ordetermination of the parts that are needed to repair the damaged vehiclewithout having to bring or tow the damaged vehicle to a repair facilityor other assessor.

Further, the techniques, systems, methods, and apparatuses of thepresent application allow the identification, prediction, ordetermination of parts needed to repair the damaged vehicle (and/orother vehicle claim information) to be performed with much greateraccuracy and much earlier in the vehicle repair process than is able tobe done by a human agent, inspector, or repair facility employee, atleast because the identifications, predictions, or determinations areobtained from an information prediction model that has been generatedbased on a plethora of historical claim data corresponding to damagedvehicles from multiple insurance carriers and other sources.Accordingly, as the predictions of the needed vehicle parts and othervehicle claim information are based on a rigorous, statistical analysisof a much wider claim data base than is known to any individual agent,inspector, or repair facility employee, and hence is more accurate, theaccuracy of parts identification increases with the techniques describedherein, and the time required to assess the vehicle damage, identify andorder needed parts, and repair the vehicle is greatly lessened. In fact,as more and more claim data is added to the historical claim data setand the information prediction model is refined over time, the accuracyof the prediction of the vehicle parts needed to repair a damagedvehicle and/or other vehicle claim information may increase to a levelwhere confidence in identifications, predictions, and determinations issignificantly increased, and indeed, is statistically accurate.

Although the disclosure describes example methods and systems including,among other components, software and/or firmware executed on hardware,it should be noted that these examples are merely illustrative andshould not be considered as limiting. For example, it is contemplatedthat any or all of the hardware, software, and firmware components couldbe embodied exclusively in hardware, exclusively in software, or in anycombination of hardware and software. Accordingly, while the disclosuredescribes example methods and apparatus, persons of ordinary skill inthe art will readily appreciate that the examples provided are not theonly way to implement such methods and apparatus.

When implemented, any of the computer readable instructions or softwaredescribed herein may be stored in any computer readable storage mediumor memory such as on a magnetic disk, a laser disk, or other storagemedium, in a RAM or ROM of a computer or processor, portable memory,etc. Likewise, this software may be delivered to a user, a process plantor an operator workstation using any known or desired delivery methodincluding, for example, on a computer readable disk or othertransportable computer storage mechanism or over a communication channelsuch as a telephone line, the Internet, the World Wide Web, any otherlocal area network or wide area network, etc. (which delivery is viewedas being the same as or interchangeable with providing such software viaa transportable storage medium). Furthermore, this software may beprovided directly without modulation or encryption or may be modulatedand/or encrypted using any suitable modulation carrier wave and/orencryption technique before being transmitted over a communicationchannel.

While the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it will be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention. It is also recognized that the specific approaches describedherein represent but some of many possible embodiments as describedabove. Consequently, the claims are properly construed to embrace allmodifications, variations and improvements that fall within the truespirit and scope of the invention, as well as substantial equivalentsthereof. Accordingly, other embodiments of the invention, although notdescribed particularly herein, are nonetheless considered to be withinthe scope of the invention.

What is claimed is:
 1. An image processing system, comprising: one ormore data storage devices comprising non-transitory, tangiblecomputer-readable media storing thereon historical claim data of aplurality of historical vehicle insurance claims, the historical claimdata including a plurality of claim attributes including image attributedata of the plurality of historical vehicle insurance claims, respectiveindications of actual replacement parts of the plurality of historicalvehicle insurance claims, and a plurality of other claim attributes ofthe plurality of historical vehicle insurance claims; an accessmechanism to the historical claim data; and one or more computingdevices comprising: a network interface via which a plurality of imagesof a damaged vehicle is received; an image attribute extractioncomponent configured to generate, by operating on the plurality ofimages of the damaged vehicle, a set of image attributes indicative of acontent of at least some of the plurality of images of the damagedvehicle, the set of image attributes including one or more imageattribute types and respective values of the one or more image attributetypes; a parts identifier component configured to generate, based on theset of image attributes indicative of the damaged vehicle, respectiveindications of one or more replacement parts needed to repair thedamaged vehicle, the parts identifier component including an informationidentification model generated by accessing, via the access mechanism,the historical claim data of the plurality of historical vehicleinsurance claims and performing a regression analysis on the accessedhistorical claim data to determine a subset of a plurality of imageattributes that are more strongly correlated to actual replacement partscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes; and anoutput interface via which the generated, respective indications of theone or more replacement parts are provided to at least one of thenetwork interface or a user interface.
 2. The image processing system ofclaim 1, wherein the plurality of historical vehicle insurance claimsincludes historical vehicle insurance claims corresponding to aplurality of insurance carriers.
 3. The image processing system of claim1, wherein the generated respective indications of the one or morereplacement parts are provided via the output interface of the imageprocessing system at First Notice of Loss (FNOL).
 4. The imageprocessing system of claim 1, wherein the set of image attributescomprises an image attribute vector including the respective values ofthe one or more image attribute types, and wherein the respective valuesof the one or more image attribute types comprise numerical values. 5.The image processing system of claim 1, wherein: the image attributeextraction component comprises a feature detection component; the one ormore image attribute types include one or more of an edge, a corner, ablob, a ridge, a feature location, a feature size, a feature dimension,an intersection of features, a feature determined based on ascale-invariant feature transformation, a feature determined based on athreshold, a feature determined based on a template match, or anotherimage attribute type; and at least some of the respective values of theone or more image attribute types are generated by the feature detectioncomponent operating on the plurality of images of the damaged vehicle.6. The image processing system of claim 1, wherein: the image attributeextraction component comprises an image transformation component; theone or more image attribute types include one or more of a scalecorresponding to one or more images, an orientation of the damagedvehicle within one or more images, a texture, an edge, a corner, apoint, a radius, a shape, a lighting orientation, a shadow orientation,a lighting location, a shadow location, or another image attribute type;and at least some of the respective values of the one or more imageattribute types are generated by the image transformation component atleast one of transforming or filtering the plurality of images of thedamaged vehicle.
 7. The image processing system of claim 1, wherein: theimage attribute extraction component comprises a metric determinationcomponent; the one or more image attribute types include one or moremetrics from a set of metrics including a statistical measure, agradient measure, and another metric; and at least some of therespective values of the one or more image attribute types arecalculated by the metric determination component operating on theplurality of images of the damaged vehicle.
 8. The image processingsystem of claim 7, wherein the one or more metrics include at least oneof: a frequency of a particular attribute within the plurality ofimages, an average of the particular attribute within the plurality ofimages, a distribution of the particular attribute within the pluralityof images, another metric corresponding to the particular attributewithin the plurality of images, an average corresponding to a color, atexture, or a contrast property within the plurality of images, agradient corresponding to the color, the texture, or the contrastproperty within the plurality of images, a frequency corresponding tothe color, the texture, or the contrast property within the plurality ofimages, a distribution corresponding to the color, the texture, or thecontrast property within the plurality of images, or another metriccorresponding to the color, the texture, or the contrast property withinthe plurality of images.
 9. The image processing system of claim 1,wherein: at least one of the one or more image attribute types includedin the set of image attributes corresponding to the damaged vehicle isan independent variable of the information identification model; therespective values of the at least one of the one or more image attributetypes included in the set of image attributes corresponding to thedamaged vehicle are provided as an input to the informationidentification model; and at least one of the respective indications ofthe one or more replacement parts needed to repair the damaged vehiclecorrespond to respective dependent variables of the informationidentification model and are received via an output of the informationidentification model.
 10. The image processing system of claim 1,wherein the parts identifier component generates the respectiveindications of the one or more replacement parts needed to repair thedamaged vehicle further based on at least one of a level of repairquality or a customer satisfaction score.
 11. The image processingsystem of claim 1, wherein the respective indications of the one or morereplacement parts needed to repair the damaged vehicle includes at leastone indication of a replacement part type selected from: OEM (OriginalEquipment Manufacturer), new, recycled, reconditioned, or anotherreplacement part type.
 12. The image processing system of claim 1,wherein the parts identifier component is configured to generaterespective indications of one or more labor types and/or labor costsneeded to repair the damaged vehicle based on the image attribute dataof the plurality of historical vehicle insurance claims; the regressionanalysis from which the information identification model is generateddetermines another subset of the plurality of claim attributes that aremore strongly correlated to actual labor types and/or labor costscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes; thehistorical claim data includes respective indications of the actuallabor types and/or labor costs of the plurality of historical vehicleinsurance claims; and the generated, respective indications of the oneor more labor types and/or labor costs are provided to the at least oneof the network interface or the user interface via the output interfaceof the image processing system.
 13. The image processing system of claim1, wherein the parts identifier component is configured to generate asettlement amount corresponding to repairing the damaged vehicle basedon the image attribute data of the plurality of historical vehicleinsurance claims; the regression analysis from which the informationidentification model is generated determines another subset of theplurality of claim attributes that are more strongly correlated toactual settlement amounts corresponding to the plurality of historicalvehicle insurance claims than are other attributes of the plurality ofclaim attributes; the historical claim data includes respectiveindications of the actual settlement amounts of the plurality ofhistorical vehicle insurance claims; and the generated settlement amountis provided to the at least one of the network interface or the userinterface via the output interface of the image processing system. 14.The image processing system of claim 1, wherein the parts identifiercomponent is configured to generate a payout amount of an insured partycorresponding to repairing the damaged vehicle based on the imageattribute data of the plurality of historical vehicle insurance claims;the regression analysis from which the information identification modelis generated determines another subset of the plurality of claimattributes that are more strongly correlated to actual payout amountscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes; thehistorical claim data includes respective indications of the actualpayout amounts of the plurality of historical vehicle insurance claims;and the generated payout amount is provided to the at least one of thenetwork interface or the user interface via the output interface of theimage processing system.
 15. The image processing system of claim 1,wherein the parts identifier component is configured to generaterespective indications of one or more respective values of one or moreother parameters corresponding to repairing the damaged vehicle based onthe image attribute data of the plurality of historical vehicleinsurance claims; the regression analysis from which the informationidentification model is generated determines another subset of theplurality of claim attributes that are more strongly correlated toactual respective values of the one or more other parameterscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes; thehistorical claim data includes respective indications of the actualrespective values of the one or more other parameters of the pluralityof historical vehicle insurance claims; and the generated, respectiveindications of the one or more respective values of the one or moreother parameters are provided to the at least one of the networkinterface or the user interface via the output interface of the imageprocessing system.
 16. The image processing system of claim 15, whereinthe one or more other parameters include at least one of: a level ofrepair quality, a customer satisfaction score, a settlement amount, atowing cost, a paint cost, a tire cost, a re-inspection occurrence, are-inspection cost, a hazardous waste disposal cost, a replacement parttype, a payout amount for an insured party, a supplement occurrence, asupplement cost, a number of labor hours, a labor cost, or a labor type.17. The image processing system of claim 1, wherein at least one of thenetwork interface, the image attribute extraction component, the partsidentifier component, or the output interface comprises hardware. 18.The image processing system of claim 17, wherein at least one of thenetwork interface, the image attribute extraction component, the partsidentifier component, or the output interface comprises software orfirmware.
 19. The image processing system of claim 18, wherein thesoftware comprises a set of computer-executable instructions stored onone or more memories, the hardware comprises the one or more memories,and the image processing system further comprises one or more processorsfor executing the set of computer-executable instructions.
 20. The imageprocessing system of claim 1, wherein at least one of (i) the set ofimage attributes corresponding to the damaged vehicle, (ii) therespective indications of the one or more replacement parts needed torepair the damaged vehicle, or (iii) respective indications of one ormore values of one or more other parameters corresponding to repairingthe damaged vehicle and generated by the parts identifier component areat least one of (a) stored in at least one of the one or more datastorage devices or in another one or more data storage devices, or (b)provided to the at least one of the network interface or the userinterface via the output interface of the image processing system.
 21. Amethod of image processing, comprising: obtaining, via a networkinterface of an image processing system, a plurality of images of adamaged vehicle; generating, by using an image attribute extractioncomponent of the image processing system operating on the plurality ofimages of the damaged vehicle, a set of image attributes indicative of acontent of at least some of the plurality of images of the damagedvehicle, the set of image attributes including one or more imageattribute types and respective values of the one or more image attributetypes; generating, by using a parts identifier component of the imageprocessing system, and based on the set of image attributes indicativeof the damaged vehicle, one or more indications of one or morereplacement parts needed to repair the damaged vehicle, the partsidentifier component including an information identification modelgenerated by performing a regression analysis on historical claim dataof a plurality of historical vehicle insurance claims, the historicalclaim data including a plurality of claim attributes including imageattribute data of the plurality of historical vehicle insurance claims,respective indications of actual replacement parts of the plurality ofhistorical vehicle insurance claims, and a plurality of other claimattributes of the plurality of historical vehicle insurance claims, andthe regression analysis to determine a subset of a plurality of imageattributes that are more strongly correlated to actual replacement partscorresponding to the plurality of historical vehicle insurance claimsthan are other attributes of the plurality of claim attributes; andproviding, via an output interface of the image processing system to arecipient, the generated, one or more indications of the one or morereplacement parts needed to repair the damaged vehicle.
 22. The methodof claim 21, wherein the method is performed at First Notice of Loss(FNOL) corresponding to the damaged vehicle.
 23. The method of claim 21,further comprising at least one of ordering the one or more replacementparts, or repairing the damaged vehicle using the one or morereplacement parts.
 24. The method of claim 21, wherein generating theset of image attributes indicative of the content of at least some ofthe plurality of images of the damaged vehicle comprises at least oneof: detecting one or more features within the plurality of images,transforming at least one image of the plurality of images, filteringthe at least one image or another image of the plurality of images, orcalculating one or more metrics for at least one feature, color,texture, or contrast property within the plurality of images.